SECF: Improving SPARQL Querying performance with proactive fetching and Caching

Wei Emma Zhang, Quan Z. Sheng, Yongrui Qin, Lina Yao, Ali Shemshadi, Kerry Taylor

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    7 Citations (Scopus)

    Abstract

    Querying on SPARQL endpoints may be unsatisfactory due to high latency of connections to the endpoints. Caching is an important way to accelerate the query response speed. In this paper, we propose SPARQL Endpoint Caching Framework (SECF), a client-side caching framework for this purpose. In particular, we prefetch and cache the results of similar queries to recently cached query aiming to improve the overall querying performance. The similarity between queries are calculated via an improved Graph Edit Distance (GED) function. We also adapt a smoothing method to implement the cache replacement. The empirical evaluations on real world queries show that our approach has great potential to enhance the cache hit rate and accelerate the querying speed on SPARQL endpoints.

    Original languageEnglish
    Title of host publication2016 Symposium on Applied Computing, SAC 2016
    PublisherAssociation for Computing Machinery
    Pages362-367
    Number of pages6
    ISBN (Electronic)9781450337397
    DOIs
    Publication statusPublished - 4 Apr 2016
    Event31st Annual ACM Symposium on Applied Computing, SAC 2016 - Pisa, Italy
    Duration: 4 Apr 20168 Apr 2016

    Publication series

    NameProceedings of the ACM Symposium on Applied Computing
    Volume04-08-April-2016

    Conference

    Conference31st Annual ACM Symposium on Applied Computing, SAC 2016
    Country/TerritoryItaly
    CityPisa
    Period4/04/168/04/16

    Fingerprint

    Dive into the research topics of 'SECF: Improving SPARQL Querying performance with proactive fetching and Caching'. Together they form a unique fingerprint.

    Cite this